Ann. Data. Sci. DOI 10.1007/s40745-017-0119-y Approximate Shortest Distance Computing Using k-Medoids Clustering Sakshi Agarwal 1 · Shikha Mehta 1 Received: 17 February 2017 / Revised: 6 April 2017 / Accepted: 13 July 2017 © Springer-Verlag GmbH Germany 2017 Abstract Shortest distance query is widely used aspect in large scale networks. Numerous approaches are present in the literature to approximate the distance between two query nodes. Most popular distance approximation approach is landmark embed- ding scheme. In this technique selection of optimal landmarks is a NP-hard problem. Various heuristics available to locate optimal landmarks include random, degree, close- ness centrality, betweenness and eccentricity etc. In this paper, we propose to employ k-medoids clustering based approach to improve distance estimation accuracy over local landmark embedding techniques. In particular, it is observed that global selection of the seed landmarks causes’ large relative error, which is further reduced using local landmark embedding. The efficacy of the proposed approach is analyzed with respect to conventional graph embedding techniques on six large-scale networks. Results express that the proposed landmark selection scheme reduces the shortest distance estimation error considerably. Proposed technique is able to reduce the approximation error of shortest distance by upto 29% with respect to the other graph embedding technique. Keywords k-Medoids · Local landmark embedding · Least common ancestor · Local search · Query optimization B Sakshi Agarwal sakshi.agarwal@jiit.ac.in Shikha Mehta shikha.mehta@jiit.ac.in 1 Computer Science and Information Technology, JIIT University, Noida, India 123